House Sale Prediction 2014-2015

1.Import, cleaning data and visualization

Import libraries

In [8]:
import numpy as np
import pprint
import matplotlib.pyplot as plt
%matplotlib inline
import pandas as pd
import seaborn as sns
from IPython.display import display
import plotly
import plotly.plotly as py
import plotly.graph_objs as go
from plotly.offline import iplot
import cufflinks.offline
import cufflinks as cf
cufflinks.offline.run_from_ipython()
sns.set(style="ticks")
cf.set_config_file(world_readable=True, theme='pearl', offline=True)
plt.rc('font', family='Verdana')

Import data and transform in to pandas dataframe

In [9]:
data = pd.read_csv('/home/shashy/Загрузки/housesalesprediction/kc_house_data.csv')
data.head()
Out[9]:
id date price bedrooms bathrooms sqft_living sqft_lot floors waterfront view ... grade sqft_above sqft_basement yr_built yr_renovated zipcode lat long sqft_living15 sqft_lot15
0 7129300520 20141013T000000 221900.0 3 1.00 1180 5650 1.0 0 0 ... 7 1180 0 1955 0 98178 47.5112 -122.257 1340 5650
1 6414100192 20141209T000000 538000.0 3 2.25 2570 7242 2.0 0 0 ... 7 2170 400 1951 1991 98125 47.7210 -122.319 1690 7639
2 5631500400 20150225T000000 180000.0 2 1.00 770 10000 1.0 0 0 ... 6 770 0 1933 0 98028 47.7379 -122.233 2720 8062
3 2487200875 20141209T000000 604000.0 4 3.00 1960 5000 1.0 0 0 ... 7 1050 910 1965 0 98136 47.5208 -122.393 1360 5000
4 1954400510 20150218T000000 510000.0 3 2.00 1680 8080 1.0 0 0 ... 8 1680 0 1987 0 98074 47.6168 -122.045 1800 7503

5 rows × 21 columns

Check the size of dataset

In [46]:
data.shape
Out[46]:
(21613, 21)

Check the info about dataset and search a null objects

In [11]:
data.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 21613 entries, 0 to 21612
Data columns (total 21 columns):
id               21613 non-null int64
date             21613 non-null object
price            21613 non-null float64
bedrooms         21613 non-null int64
bathrooms        21613 non-null float64
sqft_living      21613 non-null int64
sqft_lot         21613 non-null int64
floors           21613 non-null float64
waterfront       21613 non-null int64
view             21613 non-null int64
condition        21613 non-null int64
grade            21613 non-null int64
sqft_above       21613 non-null int64
sqft_basement    21613 non-null int64
yr_built         21613 non-null int64
yr_renovated     21613 non-null int64
zipcode          21613 non-null int64
lat              21613 non-null float64
long             21613 non-null float64
sqft_living15    21613 non-null int64
sqft_lot15       21613 non-null int64
dtypes: float64(5), int64(15), object(1)
memory usage: 3.5+ MB

Some visualization of price

In [12]:
data['price'].iplot(kind='hist', xTitle='price',
                  yTitle='count', title='Price')

Heatmap

In [13]:
f, ax = plt.subplots(figsize=(16, 12))
corr = data.corr()
sns.heatmap(corr, annot=True)
/home/shashy/anaconda3/lib/python3.7/site-packages/matplotlib/font_manager.py:1241: UserWarning:

findfont: Font family ['Verdana'] not found. Falling back to DejaVu Sans.

Out[13]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fd113f350f0>
/home/shashy/anaconda3/lib/python3.7/site-packages/matplotlib/font_manager.py:1241: UserWarning:

findfont: Font family ['Verdana'] not found. Falling back to DejaVu Sans.

Visualization price of the houses versus footage ofthe home by date

In [14]:
data.iplot(
    x='sqft_living',
    y='price',
    # Указываем категорию
    categories='date',
    xTitle='square footage of the home',
    yTitle='price of the houses',
    title='Price of The Houses vs Square Footage Of The Home by Date ')

delete unnecessary columns

In [15]:
Clear_data_X = data.drop(columns=['id', 'date'], axis=1)
Clear_data_X.head()
Out[15]:
price bedrooms bathrooms sqft_living sqft_lot floors waterfront view condition grade sqft_above sqft_basement yr_built yr_renovated zipcode lat long sqft_living15 sqft_lot15
0 221900.0 3 1.00 1180 5650 1.0 0 0 3 7 1180 0 1955 0 98178 47.5112 -122.257 1340 5650
1 538000.0 3 2.25 2570 7242 2.0 0 0 3 7 2170 400 1951 1991 98125 47.7210 -122.319 1690 7639
2 180000.0 2 1.00 770 10000 1.0 0 0 3 6 770 0 1933 0 98028 47.7379 -122.233 2720 8062
3 604000.0 4 3.00 1960 5000 1.0 0 0 5 7 1050 910 1965 0 98136 47.5208 -122.393 1360 5000
4 510000.0 3 2.00 1680 8080 1.0 0 0 3 8 1680 0 1987 0 98074 47.6168 -122.045 1800 7503

Create a discrette scatter to show the relationship between the data

In [16]:
sns.pairplot(Clear_data_X)
Out[16]:
<seaborn.axisgrid.PairGrid at 0x7fd113fa7668>

2.Splitting + Normalizing data and Predicted Models

Separate the target variable from the master data and divide the data into test and training samples.

In [17]:
X = Clear_data_X
y = data['price'].values
In [18]:
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state=42)

After the data is divided, it is necessary to normalize them with the help of a scaler

In [19]:
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
X_train[X_train.columns] = scaler.fit_transform(X_train[X_train.columns])
X_test[X_test.columns] = scaler.fit_transform(X_test[X_test.columns])
/home/shashy/anaconda3/lib/python3.7/site-packages/sklearn/preprocessing/data.py:645: DataConversionWarning:

Data with input dtype int64, float64 were all converted to float64 by StandardScaler.

/home/shashy/anaconda3/lib/python3.7/site-packages/sklearn/base.py:464: DataConversionWarning:

Data with input dtype int64, float64 were all converted to float64 by StandardScaler.

/home/shashy/anaconda3/lib/python3.7/site-packages/sklearn/preprocessing/data.py:645: DataConversionWarning:

Data with input dtype int64, float64 were all converted to float64 by StandardScaler.

/home/shashy/anaconda3/lib/python3.7/site-packages/sklearn/base.py:464: DataConversionWarning:

Data with input dtype int64, float64 were all converted to float64 by StandardScaler.

We start training our models. For training, I chose 3 models: 1.LinearRegression, 2.RidgeRegression and 3.RandomForrestRegression. As presented below, the linear models and the ensemble of solutions to a random forest coped very well with their work and made a fairly accurate forecast based on test data.

In [20]:
from sklearn.linear_model import LinearRegression
lr = LinearRegression(normalize=True).fit(X_train, y_train)
y_head_lr = lr.predict(X_test)

LinearRegression - 0.99 * 100 = 99% prediction

In [21]:
print("R^2 on train Linear Regression: {:.2f}".format(lr.score(X_train, y_train)))
print("R^2 on test Linear Regression: {:.2f}".format(lr.score(X_test, y_test)))
R^2 on train Linear Regression: 1.00
R^2 on test Linear Regression: 0.99
In [22]:
print("real value of y_test[1]: " + str(y_test[1]) + " -> the predict: " + str(lr.predict(X_test.iloc[[1],:])))
print("real value of y_test[2]: " + str(y_test[2]) + " -> the predict: " + str(lr.predict(X_test.iloc[[2],:])))
real value of y_test[1]: 865000.0 -> the predict: [831189.4227456]
real value of y_test[2]: 1038000.0 -> the predict: [992015.34777363]

RidgeRegression (with params: alpha = 10) - 0.99 * 100 = 99% prediction

In [23]:
from sklearn.linear_model import Ridge
ridge = Ridge(alpha = 10).fit(X_train, y_train)
In [24]:
print("real value of y_test[1]: " + str(y_test[1]) + " -> the predict: " + str(ridge.predict(X_test.iloc[[1],:])))
print("real value of y_test[2]: " + str(y_test[2]) + " -> the predict: " + str(ridge.predict(X_test.iloc[[2],:])))
real value of y_test[1]: 865000.0 -> the predict: [831009.22805452]
real value of y_test[2]: 1038000.0 -> the predict: [992422.2099911]
In [25]:
from sklearn.metrics import r2_score
y_head_ridge = ridge.predict(X_test)
r2score1 = r2_score(y_test, y_head_ridge)
print('R^2 score on ridge: {:.2f}'.format(r2score1))
R^2 score on ridge: 0.99

RandomForrestRegressor (with params: n_jobs = -1, n_estimators = 10, verbose = 3) - 0.99 * 100 = 99% predict

In [30]:
from sklearn.ensemble import RandomForestRegressor
rf = RandomForestRegressor(n_jobs=-1, n_estimators=10, verbose=3)
rf.fit(X_train,y_train)
y_head_rf = rf.predict(X_test)
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print("real value of y_test[1]: " + str(y_test[1]) + " -> the predict: " + str(rf.predict(X_test.iloc[[1],:])))
print("real value of y_test[2]: " + str(y_test[2]) + " -> the predict: " + str(rf.predict(X_test.iloc[[2],:])))
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real value of y_test[1]: 865000.0 -> the predict: [831204.8]
real value of y_test[2]: 1038000.0 -> the predict: [991088.]
In [37]:
print("R^2 score of RandomForrestRegression is: {:.2f}".format(r2_score(y_test, y_head_rf)))
R^2 score of RandomForrestRegression is: 0.99

And below is the final accuracy chart of all 3 models.

In [49]:
f, ax = plt.subplots(figsize=(16,10))
y = np.array([r2_score(y_test,y_head_lr),r2_score(y_test,y_head_ridge), r2_score(y_test, y_head_rf)])
x = ["LinearRegression","RidgeReg.", "RandomForrestReg"]
plt.bar(x,y)
plt.title("Accuracy of the models")
Out[49]:
Text(0.5, 1.0, 'Accuracy of the models')

Also, I saved the first 100 parameters of each model in the lists. From these lists, you can safely withdraw any value from 1-100

In [43]:
settings = range(1, 101)
lr_predict = []
ridge_predict = []
rf_predict = []
for n_settings in settings:
    lr_predict.append("real value of LinearRegression on y_test: " + str(y_test[n_settings]) + " -> the predict: " + str(lr.predict(X_test.iloc[[n_settings],:])))
    ridge_predict.append("real value of RidgeRegression on y_test: " + str(y_test[n_settings]) + " -> the predict: " + str(ridge.predict(X_test.iloc[[n_settings],:])))
    rf_predict.append("real value of RandomForrestREgression on y_test: " + str(y_test[n_settings]) + " -> the predict: " + str(rf.predict(X_test.iloc[[n_settings],:])))
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[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.
[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done   7 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished
[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.
[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done   7 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished
[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.
[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done   7 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished
[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.
[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done   7 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished
[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.
[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done   7 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished
[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.
[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done   7 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished
[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.
[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done   7 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished
[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.
[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done   7 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished
[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.
[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done   7 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished
[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.
[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done   7 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished
[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.
[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done   7 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished
[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.
[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done   7 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished
[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.
[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done   7 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished
[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.
[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done   7 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished
[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.
[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done   7 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished
[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.
[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done   7 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished
[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.
[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done   7 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished
[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.
[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done   7 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished
[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.
[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done   7 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished
[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.
[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done   7 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished
[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.
[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done   7 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished
[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.
[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done   7 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished
[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.
[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done   7 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished
[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.
[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done   7 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished
[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.
[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done   7 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished
[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.
[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done   7 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished
[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.
[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done   7 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished
[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.
[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done   7 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished
[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.
[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done   7 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished
[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.
[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done   7 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished
[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.
[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done   7 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished
[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.
[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done   7 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished
[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.
[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done   7 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished
[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.
[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done   7 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished
[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.
[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done   7 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished
[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.
[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done   7 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished
[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.
[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done   7 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished
[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.
[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done   7 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished
[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.
[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done   7 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished
[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.
[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done   7 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished
[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.
[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done   7 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished
[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.
[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done   7 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished
[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.
[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done   7 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished
[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.
[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done   7 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished
[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.
[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done   7 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished
[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.
[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done   7 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished
[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.
[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done   7 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished
[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.
[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done   7 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished
[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.
[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done   7 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished
[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.
[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done   7 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished
[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.
[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done   7 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished
[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.
[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done   7 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished
[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.
[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done   7 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished
[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.
[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done   7 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished
[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.
[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done   7 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished
[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.
[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done   7 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished
[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.
[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done   7 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished
[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.
[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done   7 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished
[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.
[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done   7 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished
[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.
[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done   7 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished
[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.
[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done   7 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished
[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.
[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done   7 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished
[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.
[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done   7 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished
[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.
[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done   7 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished
[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.
[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done   7 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished
[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.
[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done   7 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished
[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.
[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done   7 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished
[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.
[Parallel(n_jobs=10)]: Done   3 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done   7 out of  10 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=10)]: Done  10 out of  10 | elapsed:    0.0s finished
In [50]:
print(lr_predict[1])
real value of LinearRegression on y_test: 1038000.0 -> the predict: [992015.34777363]

3.Conclusion

Thus, we obtained fairly clear machine learning models with an accuracy of 99%. They made a visualization, cleared and divided the data into training and test samples and then normalized the data.